Context structure for the algorithm called Anderson acceleration. More...
#include <cs_iter_algo.h>
 Collaboration diagram for cs_iter_algo_aac_t:
 Collaboration diagram for cs_iter_algo_aac_t:| Data Fields | |
| cs_iter_algo_param_aac_t | param | 
| cs_sles_convergence_state_t | cvg_status | 
| double | normalization | 
| double | tol | 
| double | prev_res | 
| double | res | 
| double | res0 | 
| int | n_algo_iter | 
| int | n_inner_iter | 
| int | last_inner_iter | 
| cs_lnum_t | n_elts | 
| Work quantities (temporary) | |
| int | n_dir | 
| cs_real_t * | fold | 
| cs_real_t * | df | 
| cs_real_t * | gold | 
| cs_real_t * | dg | 
| cs_real_t * | Q | 
| cs_sdm_t * | R | 
| cs_real_t * | gamma | 
Context structure for the algorithm called Anderson acceleration.
Set of parameters and arrays to manage the Anderson acceleration
| cvg_status | 
Converged, iterating or diverged status
| df | 
Difference between the current and previous values of f
| dg | 
Difference between the current and previous values of g
| fold | 
Previous values for f
| gamma | 
Coefficients used to define the linear combination
| gold | 
Previous values for g
| last_inner_iter | 
Last number of iterations for the inner solver
| n_algo_iter | 
Current number of iterations for the algorithm (outer iterations)
| n_dir | 
Number of directions currently at stake
| cs_lnum_t n_elts | 
| n_inner_iter | 
Curent cumulated number of inner iterations (sum over the outer iterations)
| normalization | 
Value of the normalization for the relative tolerance.
The stopping criterion is such that res < rtol * normalization. By default, the normalization is set to 1.
| param | 
Set of parameters driving the behavior of the Anderson acceleration
| prev_res | 
Value of the previous residual achieved during the iterative process
| Q | 
Matrix Q in the Q.R factorization (seen as a bundle of vectors)
| R | 
Matrix R in the Q.R factorization (small dense matrix)
| res | 
Value of the residual for the iterative algorithm
| res0 | 
Value of the first residual of the iterative process. This is used for detecting the divergence of the algorithm.
| tol | 
Tolerance computed as tol = max(atol, normalization*rtol) where atol and rtol are respectively the absolute and relative tolerance associated to the algorithm